Broadcasting is a process for transmitting audio/video signals from the transmitter to the receiver. The transmission of signals provides accessibility of contents to the receiver placed in any household environment. For example, the communication device such as set-top box, in case of a television program, is configured to receive the signal from the transmitter and provide accessibility of the contents by using a television set. The most commonly used set-top boxes are adapted to receive encoded/compressed digital signals from the signal source and decode those signals into analog signals.
In current scenario televised entertainment broadcasting has seen a proliferation of new channel launches and they are added in one or more genres of hundreds of preexisting channels.
The genres are basically categorized according to age, interest and subject of broadcasted contents. The user has to browse through the graphical user interface to access his program of interest. Browsing for various channels will lead to increased duration of time for user to get to the channel of his interest. The set top box is configured with various software applications, recommender system to enable user in reducing time to access the preferred channels.
The recommender system is configured to recommend the user's interest by predicting the rating or preferences of the user. The recommender system uses various approaches such as content based approach, collaborative based approach and hybrid based approach to recommend the program. The most common approach used by the recommender system is to learn the behavior of the user. In such an approach, the user's viewing behavior is stored for a watched program and if the recommender system is based on the learned behavior only, and in case a program of interest to the user is not scheduled for a month, the interest of the user for a particular program will not be captured and stored and thus when after a month of training the program of interest to the user is available, the recommender system will not recommend the program of user interest.
For example consider a user interested in tennis, but there is no major tennis match scheduled for the next one month. During this month, the user's behavior does not reflect the user's interest in tennis. Thus when a tennis match is scheduled, the match is not recommended to the user.
There thus exists a need for the system to recommend programs to the user based on the user specified preferences.
The system facility of prompting the user to update the preferences if the system detects that the user's preferences are no longer relevant to his behavior is also unaddressed in the prior art.
In some of the prior arts, references to certain aspects related to learning behavior are disclosed. However, the recommendation based on the user specified preferences and a procedure to prompt the user for significant changes in user's profile still remains unaddressed.
A US Patent Application US20090018845 by Morel et al mentions content alarm processing before recommending to user on a basis of the current content, but, it doesn't disclose the ways of prompting the user for significant changes in user's profile.
In U.S. Pat. No. 8,073,794, Amer-Yahia, et al mentions about how items or people of potential interest to users may be determined based at least in part on implied social network information, but, it doesn't disclose a recommendation system that is based on user specified preferences. Also there is no mention of prompting the user of significant changes in user's profile.
In “Towards TV Recommender System: Experiments with User Modeling” (IEEE Transactions on Consumer Electronics, Vol 56, No 3, August 2010), the author describes a TV recommender system based on behavior learning using cluster hypothesis. However, it does not consider stated user preferences and context-aware recommendations.
In U.S. patent application Ser. No. 12/266,273, the system do not report ability to detect change in user's preferences and guide the user to make changes in preferences.
Thus there exists a need for a recommendation system that recommends programs based on user preferences, learned user behavior and contextual information and further prompts the user for significant changes in the user's profile.